The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
In many cases, clouds in the atmosphere partially or fully obscure a satellite sensor's view of the Earth's surface. The clouds may also cast shadows on the ground where less sunlight is reflected to the sensor. In both cases, the clouds limit the information a remote sensing observer may obtain about the surface and compromise estimates of physical parameters obtained from the sensors. For many applications of remotely sensed imagery, it is therefore critical to be able to identify these affected pixels, usually for exclusion from analysis. It is conventional to distribute alongside the imagery a separate raster band called a “mask”, containing discrete categorical values for each pixel location. For example, a binary mask that marks each pixel as usable versus compromised, cloud vs. ground, shadow vs. not shadow, and so forth.
The remote sensing community has proposed many cloud detection methods. For example, the Automated Cloud Cover Assignment (ACCA) system applies a number of spectral filters with pre-selected thresholds and works well for estimating the overall percentage of clouds in each scene. However, the ACCA system does not provide the cloud locations within the image, which is important for developing a mask for automated land analysis. The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) uses a two pass algorithm that includes a thermal test which generates a mask for clouds. Besides thermal bands, the algorithm also requires other ancillary data such as surface temperate. The Fmask algorithm applies rules based on physical properties to reflectance and brightness temperature (BT) to derive a potential cloud layer. Application-based thresholds can be specified by users to make their own decisions for defining a cloud region. However, the aforementioned techniques rely on thermal bands, which are not available in some types of satellite images, such as RapidEye images which presently provide data for only the visible and near-infrared (NIR) bands. Another technique is to employ a random forest model on a designated 2D histogram of band indices. The aforementioned method achieves good performance, but only provides cloud masks for low-resolution patches of 100 m by 100 m rather than at the level of individual pixels. Another technique is to use a time series of multiple scenes captured to model a pixel's biophysical change over time and to detect clouds as high-valued outliers. However, this method requires the images of the monitored area to be taken multiple times over a relatively short time period. Therefore, the aforementioned technique cannot be applied effectively to temporarily sparse images. For example, satellite image providers are not always capable of taking images of an area on demand, for instance due to the availability of a satellite with proper positioning, thus there may be a significant delay from one image to the next.
Shadows cast by thick clouds on the ground also interfere with most remote sensing applications by reducing the amount of light reflected to the satellite sensor. Simple pixel-based detection methods often falsely identify dark surfaces as cloud shadows or exclude shadows that are not dark enough. Geometry-based sensor techniques can avoid such problems and identify shadows more accurately, although those techniques often rely on a robust and accurate cloud detection process. One technique is to use lapse rate to estimate cloud top height and use the cloud pixels to cast shadows. This method works well for thick clouds, but is not accurate when the clouds are semitransparent. Another technique uses the scattering differences between short wavelength and NIR bands to produce shadow masks in Moderate Resolution Imaging Spectroradiometer (MODIS) images. However, this technique is less accurate when the shadow falls on bright surfaces or is generated by an optically thin cloud.